论文信息 - Gene Extraction for Cancer Diagnosis by Support Vector Machines

Gene Extraction for Cancer Diagnosis by Support Vector Machines

A cancer diagnosis by using the DNA microarray data faces many challenges the most serious one being the presence of thousands of genes and only several dozens (at the best) of patient’s samples. Thus, making any kind of classification in high-dimensional spaces from a limited number of data is both an extremely difficult and a prone to an error procedure. The improved Recursive Feature Elimination with Support Vector Machines (RFE-SVMs) is introduced and used here for an elimination of less relevant genes and just for a reduction of the overall number of genes used in a medical diagnostic. The paper shows why and how the, usually neglected, penalty parameter C influence classification results and the gene selection of RFE-SVMs. With an appropriate parameter C chosen, the reduction in a diagnosis error is as high as 37% on the colon cancer data set. The results suggest that with a properly chosen parameter C, the extracted genes and the constructed classifier will ensure less over-fitting of the training data leading to an increase accuracy in selecting relevant genes.

Vojislav Kecman | Te Ming Huang

[1] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[2] Geoffrey J McLachlan,et al. Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[4] Vojislav Kecman,et al. Bias Term b in SVMs Again , 2004, ESANN.

[5] D. Edwards,et al. Statistical Analysis of Gene Expression Microarray Data , 2003 .

[6] Vojislav Kecman,et al. On the equality of kernel AdaTron and sequential minimal optimization in classification and regression tasks and alike algorithms for kernel machines , 2003, ESANN.

[7] Vojislav Kecman,et al. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[8] R. Tibshirani,et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[9] Alain Rakotomamonjy,et al. Variable Selection Using SVM-based Criteria , 2003, J. Mach. Learn. Res..